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% checked
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% checked
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% checked
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% checked
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@article{hauberg_geometric_nodate,
title = {A Geometric take on Metric Learning},
pages = {9},
author = {Hauberg, Søren and Freifeld, Oren and Black, Michael J},
file = {Hauberg et al. - A Geometric take on Metric Learning.pdf:/home/clement/Zotero/storage/ZFMCUA2N/Hauberg et al. - A Geometric take on Metric Learning.pdf:application/pdf},
}

@inproceedings{shao_riemannian_2018,
location = {Salt Lake City, {UT}, {USA}},
title = {The Riemannian Geometry of Deep Generative Models},
isbn = {978-1-5386-6100-0},
doi = {10.1109/CVPRW.2018.00071},
eventtitle = {2018 {IEEE}/{CVF} Conference on Computer Vision and Pattern Recognition Workshops ({CVPRW})},
pages = {428--4288},
booktitle = {2018 {IEEE}/{CVF} Conference on Computer Vision and Pattern Recognition Workshops ({CVPRW})},
publisher = {{IEEE}},
author = {Shao, Hang and Kumar, Abhishek and Fletcher, P. Thomas},
year = {2018},
file = {Shao et al. - 2018 - The Riemannian Geometry of Deep Generative Models.pdf:/home/clement/Zotero/storage/V6ZF4836/Shao et al. - 2018 - The Riemannian Geometry of Deep Generative Models.pdf:application/pdf},
}

% checked
@article{falorsi_explorations_2018,
title = {Explorations in Homeomorphic Variational Auto-Encoding},
journal = {{arXiv}:1807.04689 [cs, stat]},
author = {Falorsi, Luca and de Haan, Pim and Davidson, Tim R. and De Cao, Nicola and Weiler, Maurice and Forré, Patrick and Cohen, Taco S.},
year = {2018},
eprinttype = {arxiv},
eprint = {1807.04689},
keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
annotation = {Comment: 16 pages, 8 figures, {ICML} workshop on Theoretical Foundations and Applications of Deep Generative Models},
file = {Falorsi et al. - 2018 - Explorations in Homeomorphic Variational Auto-Enco.pdf:/home/clement/Zotero/storage/5QDW724S/Falorsi et al. - 2018 - Explorations in Homeomorphic Variational Auto-Enco.pdf:application/pdf},
}

@inproceedings{chen_metrics_2018,
  title={Metrics for deep generative models},
  author={Chen, Nutan and Klushyn, Alexej and Kurle, Richard and Jiang, Xueyan and Bayer, Justin and Smagt, Patrick},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  pages={1540--1550},
  year={2018},
  organization={PMLR}
}

% checked
@article{chadebec_geometry-aware_2020,
title = {Geometry-Aware Hamiltonian Variational Auto-Encoder},
journal = {{arXiv}:2010.11518 [cs, math, stat]},
author = {Chadebec, Clément and Mantoux, Clément and Allassonnière, Stéphanie},
year = {2020},
eprinttype = {arxiv},
eprint = {2010.11518},
keywords = {Computer Science - Machine Learning, Statistics - Machine Learning, Mathematics - Differential Geometry, Mathematics - Statistics Theory},
annotation = {Comment: 44 pages, 23 figures},
file = {Chadebec et al. - 2020 - Geometry-Aware Hamiltonian Variational Auto-Encode.pdf:/home/clement/Zotero/storage/GY9QGW7T/Chadebec et al. - 2020 - Geometry-Aware Hamiltonian Variational Auto-Encode.pdf:application/pdf},
}

@article{chadebec_data_2021,
	title = {Data {Augmentation} in {High} {Dimensional} {Low} {Sample} {Size} {Setting} {Using} a {Geometry}-{Based} {Variational} {Autoencoder}},
	journal = {arXiv preprint arXiv:2105.00026},
	author = {Chadebec, Clément and Thibeau-Sutre, Elina and Burgos, Ninon and Allassonnière, Stéphanie},
	year = {2021},
	file = {Chadebec et al. - 2021 - Data Augmentation in High Dimensional Low Sample S.pdf:/home/clement/Zotero/storage/L4LX7PLF/Chadebec et al. - 2021 - Data Augmentation in High Dimensional Low Sample S.pdf:application/pdf},
}

@article{stoll_integration_nodate,
title = {Integration and Manifolds},
pages = {68},
author = {Stoll, Michael},
file = {Stoll - Integration and Manifolds.pdf:/home/clement/Zotero/storage/HXM9XTWG/Stoll - Integration and Manifolds.pdf:application/pdf},
}

% checked
@article{kingma_auto-encoding_2014,
title = {Auto-Encoding Variational Bayes},
journal = {{arXiv}:1312.6114 [cs, stat]},
author = {Kingma, Diederik P. and Welling, Max},
year = {2014},
eprinttype = {arxiv},
eprint = {1312.6114},
keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
file = {Kingma and Welling - 2014 - Auto-Encoding Variational Bayes.pdf:/home/clement/Zotero/storage/EMK8XYE9/Kingma and Welling - 2014 - Auto-Encoding Variational Bayes.pdf:application/pdf},
}

% checked
@article{doersch_tutorial_2016,
title = {{Tutorial on Variational Autoencoders}},
journal = {{arXiv}:1606.05908 [cs, stat]},
author = {Doersch, Carl},
year = {2016},
eprinttype = {arxiv},
eprint = {1606.05908},
keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
file = {Doersch - 2016 - Tutorial on Variational Autoencoders.pdf:/home/clement/Zotero/storage/QLFU2HZY/Doersch - 2016 - Tutorial on Variational Autoencoders.pdf:application/pdf},
}

@inproceedings{davidson_hyperspherical_2018,
  title={Hyperspherical variational auto-encoders},
  author={Davidson, Tim R and Falorsi, Luca and De Cao, Nicola and Kipf, Thomas and Tomczak, Jakub M},
  booktitle={34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018},
  pages={856--865},
  year={2018},
  organization={Association For Uncertainty in Artificial Intelligence (AUAI)}
}


@article{mathieu_riemannian_nodate,
title = {Riemannian Continuous Normalizing Flows},
pages = {13},
author = {Mathieu, Emile and Nickel, Maximilian},
file = {Mathieu and Nickel - Riemannian Continuous Normalizing Flows.pdf:/home/clement/Zotero/storage/TU7XUYCK/Mathieu and Nickel - Riemannian Continuous Normalizing Flows.pdf:application/pdf},
}

@article{nielsen_survae_nodate,
title = {{SurVAE} Flows: Surjections to Bridge the Gap between {VAEs} and Flows},
pages = {12},
author = {Nielsen, Didrik and Jaini, Priyank and Hoogeboom, Emiel and Winther, Ole and Welling, Max},
file = {Nielsen et al. - SurVAE Flows Surjections to Bridge the Gap betwee.pdf:/home/clement/Zotero/storage/AI6IFQE9/Nielsen et al. - SurVAE Flows Surjections to Bridge the Gap betwee.pdf:application/pdf},
}

% checked
@article{zhao_towards_2017,
title = {Towards Deeper Understanding of Variational Autoencoding Models},
journal = {{arXiv}:1702.08658 [cs, stat]},
author = {Zhao, Shengjia and Song, Jiaming and Ermon, Stefano},
year = {2017},
eprinttype = {arxiv},
eprint = {1702.08658},
keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
file = {Zhao et al. - 2017 - Towards Deeper Understanding of Variational Autoen.pdf:/home/clement/Zotero/storage/VG9ZQC6W/Zhao et al. - 2017 - Towards Deeper Understanding of Variational Autoen.pdf:application/pdf},
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@inproceedings{arvanitidis_latent_2018,
  title={Latent space oddity: On the curvature of deep generative models},
  author={Arvanitidis, Georgios and Hansen, Lars Kai and Hauberg, S{\"o}ren},
  booktitle={6th International Conference on Learning Representations, ICLR 2018},
  year={2018}
}

% checked
@article{ovinnikov_poincare_2020,
title = {Poincar{\'e} Wasserstein Autoencoder},
journal = {{arXiv}:1901.01427 [cs, stat]},
author = {Ovinnikov, Ivan},
year = {2020-03-16},
eprinttype = {arxiv},
eprint = {1901.01427},
keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
file = {Ovinnikov - 2020 - Poincar'e Wasserstein Autoencoder.pdf:/home/clement/Zotero/storage/9MXXRPDE/Ovinnikov - 2020 - Poincar'e Wasserstein Autoencoder.pdf:application/pdf},
}

@article{mathieu_continuous_nodate,
title = {Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders},
pages = {12},
author = {Mathieu, Emile and Lan, Charline Le and Maddison, Chris J and Tomioka, Ryota and Teh, Yee Whye}
}

@article{grattarola_adversarial_2019,
title = {Adversarial autoencoders with constant-curvature latent manifolds},
volume = {81},
issn = {15684946},
doi = {10.1016/j.asoc.2019.105511},
pages = {105511},
journal = {Applied Soft Computing},
author = {Grattarola, Daniele and Livi, Lorenzo and Alippi, Cesare},
year = {2019-08},
file = {Grattarola et al. - 2019 - Adversarial autoencoders with constant-curvature l.pdf:/home/clement/Zotero/storage/EI4U4Y5R/Grattarola et al. - 2019 - Adversarial autoencoders with constant-curvature l.pdf:application/pdf},
}

% checked
@article{arvanitidis_geometrically_2020,
title = {Geometrically Enriched Latent Spaces},
journal = {{arXiv}:2008.00565 [cs, stat]},
author = {Arvanitidis, Georgios and Hauberg, Søren and Schölkopf, Bernhard},
year = {2020-08-02},
eprinttype = {arxiv},
eprint = {2008.00565},
keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
file = {Arvanitidis et al. - 2020 - Geometrically Enriched Latent Spaces.pdf:/home/clement/Zotero/storage/CKJEALTH/Arvanitidis et al. - 2020 - Geometrically Enriched Latent Spaces.pdf:application/pdf},
}

% checked
@article{arjovsky_wasserstein_2017,
title = {Wasserstein {GAN}},
journal = {{arXiv}:1701.07875 [cs, stat]},
author = {Arjovsky, Martin and Chintala, Soumith and Bottou, Léon},
year = {2017-12-06},
eprinttype = {arxiv},
eprint = {1701.07875},
keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
file = {Arjovsky et al. - 2017 - Wasserstein GAN.pdf:/home/clement/Zotero/storage/IGTKTYXC/Arjovsky et al. - 2017 - Wasserstein GAN.pdf:application/pdf},
}

@inproceedings{mallasto_wrapped_2018,
location = {Salt Lake City, {UT}},
title = {Wrapped Gaussian Process Regression on Riemannian Manifolds},
isbn = {978-1-5386-6420-9},
doi = {10.1109/CVPR.2018.00585},
eventtitle = {2018 {IEEE}/{CVF} Conference on Computer Vision and Pattern Recognition ({CVPR})},
pages = {5580--5588},
booktitle = {2018 {IEEE}/{CVF} Conference on Computer Vision and Pattern Recognition},
publisher = {{IEEE}},
author = {Mallasto, Anton and Feragen, Aasa},
year = {2018},
file = {Mallasto and Feragen - 2018 - Wrapped Gaussian Process Regression on Riemannian .pdf:/home/clement/Zotero/storage/FMZI6W3F/Mallasto and Feragen - 2018 - Wrapped Gaussian Process Regression on Riemannian .pdf:application/pdf},
}

@inproceedings{wu_data_2019,
title = {Data Augmentation Using Variational Autoencoder for Embedding Based Speaker Verification},
doi = {10.21437/Interspeech.2019-2248},
eventtitle = {Interspeech 2019},
pages = {1163--1167},
booktitle = {Interspeech 2019},
publisher = {{ISCA}},
author = {Wu, Zhanghao and Wang, Shuai and Qian, Yanmin and Yu, Kai},
year = {2019},
file = {Wu et al. - 2019 - Data Augmentation Using Variational Autoencoder fo.pdf:/home/clement/Zotero/storage/R7T35V2A/Wu et al. - 2019 - Data Augmentation Using Variational Autoencoder fo.pdf:application/pdf},
}

@inproceedings{hsu_unsupervised_2017,
  title={Unsupervised domain adaptation for robust speech recognition via variational autoencoder-based data augmentation},
  author={Hsu, Wei-Ning and Zhang, Yu and Glass, James},
  booktitle={2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)},
  pages={16--23},
  year={2017},
  organization={IEEE}
}


@inproceedings{nishizaki_data_2017,
location = {Kuala Lumpur},
title = {Data augmentation and feature extraction using variational autoencoder for acoustic modeling},
isbn = {978-1-5386-1542-3},
doi = {10.1109/APSIPA.2017.8282225},
eventtitle = {2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference ({APSIPA} {ASC})},
pages = {1222--1227},
booktitle = {2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference ({APSIPA} {ASC})},
publisher = {{IEEE}},
author = {Nishizaki, Hiromitsu},
year = {2017},
file = {Nishizaki - 2017 - Data augmentation and feature extraction using var.pdf:/home/clement/Zotero/storage/9MPWV3SS/Nishizaki - 2017 - Data augmentation and feature extraction using var.pdf:application/pdf},
}

@article{sandfort_data_2019,
title = {Data augmentation using generative adversarial networks ({CycleGAN}) to improve generalizability in {CT} segmentation tasks},
volume = {9},
issn = {2045-2322},
doi = {10.1038/s41598-019-52737-x},
pages = {16884},
number = {1},
journal = {Scientific reports},
author = {Sandfort, Veit and Yan, Ke and Pickhardt, Perry J. and Summers, Ronald M.},
year = {2019},
file = {Sandfort et al. - 2019 - Data augmentation using generative adversarial net.pdf:/home/clement/Zotero/storage/K6ZUMKGX/Sandfort et al. - 2019 - Data augmentation using generative adversarial net.pdf:application/pdf},
}

@article{xie_unsupervised_2020,
  title={Unsupervised Data Augmentation for Consistency Training},
  author={Xie, Qizhe and Dai, Zihang and Hovy, Eduard and Luong, Thang and Le, Quoc},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  year={2020}
}

% checked
@article{ruiz_unbiased_2020,
title = {Unbiased Gradient Estimation for Variational Auto-Encoders using Coupled Markov Chains},
journal = {{arXiv}:2010.01845 [cs, stat]},
author = {Ruiz, Francisco J. R. and Titsias, Michalis K. and Cemgil, Taylan and Doucet, Arnaud},
year = {2020-10-05},
eprinttype = {arxiv},
eprint = {2010.01845},
keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
annotation = {Comment: 16 pages, 4 figures},
file = {Ruiz et al. - 2020 - Unbiased Gradient Estimation for Variational Auto-.pdf:/home/clement/Zotero/storage/FGVLDVLP/Ruiz et al. - 2020 - Unbiased Gradient Estimation for Variational Auto-.pdf:application/pdf},
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@inproceedings{cohen_emnist_2017,
  title={EMNIST: Extending MNIST to handwritten letters},
  author={Cohen, Gregory and Afshar, Saeed and Tapson, Jonathan and Van Schaik, Andre},
  booktitle={2017 International Joint Conference on Neural Networks (IJCNN)},
  pages={2921--2926},
  year={2017},
  organization={IEEE}
}

@inproceedings{huang_densely_2017,
location = {Honolulu, {HI}},
title = {Densely Connected Convolutional Networks},
isbn = {978-1-5386-0457-1},
doi = {10.1109/CVPR.2017.243},
eventtitle = {2017 {IEEE} Conference on Computer Vision and Pattern Recognition ({CVPR})},
pages = {2261--2269},
booktitle = {2017 {IEEE} Conference on Computer Vision and Pattern Recognition ({CVPR})},
publisher = {{IEEE}},
author = {Huang, Gao and Liu, Zhuang and Van Der Maaten, Laurens and Weinberger, Kilian Q.},
year = {2017},
file = {Huang et al. - 2017 - Densely Connected Convolutional Networks.pdf:/home/clement/Zotero/storage/ZZABLZCN/Huang et al. - 2017 - Densely Connected Convolutional Networks.pdf:application/pdf},
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@misc{amos_bamosdensenetpytorch_2020,
	title = {bamos/densenet.pytorch},
	url = {https://github.com/bamos/densenet.pytorch},
	copyright = {Apache-2.0 License},
	abstract = {A PyTorch implementation of DenseNet.},
	author = {Amos, Brandon},
	year = {2020},
	note = {original-date: 2017-02-09T15:33:23Z},
	keywords = {deep-learning, densenet, pytorch},
}


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title = {Random forests},
volume = {45},
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note = {Issue: 2},
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@article{kotsiantis_supervised_2007,
title = {Supervised machine learning: A review of classification techniques},
volume = {160},
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@inproceedings{kalatzis_variational_2020,
  title={Variational Autoencoders with Riemannian Brownian Motion Priors},
  author={Kalatzis, Dimitrios and Eklund, David and Arvanitidis, Georgios and Hauberg, Soren},
  booktitle={International Conference on Machine Learning},
  pages={5053--5066},
  year={2020},
  organization={PMLR}
}

@inproceedings{tomczak_vae_2018,
  title={VAE with a VampPrior},
  author={Tomczak, Jakub and Welling, Max},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  pages={1214--1223},
  year={2018},
  organization={PMLR}
}

@inproceedings{bauer_resampled_2019,
  title={Resampled priors for variational autoencoders},
  author={Bauer, Matthias and Mnih, Andriy},
  booktitle={The 22nd International Conference on Artificial Intelligence and Statistics},
  pages={66--75},
  year={2019},
  organization={PMLR}
}

@article{klushyn_learning_2019,
	title = {Learning {Hierarchical} {Priors} in {VAEs}},
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@misc{yburda_yburdaiwae_2020,
title = {yburda/iwae},
abstract = {Code to train Importance Weighted Autoencoders on {MNIST} and {OMNIGLOT}},
author = {yburda},
year = {2020},
note = {original-date: 2015-09-08T15:28:40Z},
}

% checked
@article{burda_importance_2016,
title = {Importance Weighted Autoencoders},
journal = {{arXiv}:1509.00519 [cs, stat]},
author = {Burda, Yuri and Grosse, Roger and Salakhutdinov, Ruslan},
year = {2016-11-07},
eprinttype = {arxiv},
eprint = {1509.00519},
keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
annotation = {Comment: Submitted to {ICLR} 2015},
file = {Burda et al. - 2016 - Importance Weighted Autoencoders.pdf:/home/clement/Zotero/storage/HMECVG37/Burda et al. - 2016 - Importance Weighted Autoencoders.pdf:application/pdf},
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@article{higgins_-vae_2017,
title = {{β-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework}},
pages = {22},
author = {Higgins, Irina and Matthey, Loic and Pal, Arka and Burgess, Christopher and Glorot, Xavier and Botvinick, Matthew and Mohamed, Shakir and Lerchner, Alexander},
year = {2017},
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@incollection{lintas_biomedical_2017,
title = {Biomedical Data Augmentation Using Generative Adversarial Neural Networks},
volume = {10614},
series = {LNCS},
isbn = {978-3-319-68611-0 978-3-319-68612-7},
pages = {626--634},
booktitle = {Artificial Neural Networks and Machine Learning – {ICANN} 2017},
publisher = {Springer International Publishing},
author = {Calimeri, Francesco and Marzullo, Aldo and Stamile, Claudio and Terracina, Giorgio},
editor = {Lintas, Alessandra and Rovetta, Stefano and Verschure, Paul F.M.J. and Villa, Alessandro E.P.},
year = {2017},
doi = {10.1007/978-3-319-68612-7_71},
file = {Calimeri et al. - 2017 - Biomedical Data Augmentation Using Generative Adve.pdf:/home/clement/Zotero/storage/9G28URMJ/Calimeri et al. - 2017 - Biomedical Data Augmentation Using Generative Adve.pdf:application/pdf},
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% checked
@article{antoniou_data_2018,
title = {Data Augmentation Generative Adversarial Networks},
journal = {{arXiv}:1711.04340 [cs, stat]},
author = {Antoniou, Antreas and Storkey, Amos and Edwards, Harrison},
year = {2018-03-21},
eprinttype = {arxiv},
eprint = {1711.04340},
keywords = {Computer Science - Machine Learning, Statistics - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Neural and Evolutionary Computing},
annotation = {Comment: 10 pages},
file = {Antoniou et al. - 2018 - Data Augmentation Generative Adversarial Networks.pdf:/home/clement/Zotero/storage/HEB2H6GY/Antoniou et al. - 2018 - Data Augmentation Generative Adversarial Networks.pdf:application/pdf},
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@incollection{wu_conditional_2018,
  title={Conditional infilling GANs for data augmentation in mammogram classification},
  author={Wu, Eric and Wu, Kevin and Cox, David and Lotter, William},
  booktitle={Image analysis for moving organ, breast, and thoracic images},
  pages={98--106},
  year={2018},
  publisher={Springer}
}

@article{frid-adar_gan-based_2018,
title = {{GAN}-based synthetic medical image augmentation for increased {CNN} performance in liver lesion classification},
volume = {321},
issn = {0925-2312},
pages = {321--331},
journal = {Neurocomputing},
author = {Frid-Adar, Maayan and Diamant, Idit and Klang, Eyal and Amitai, Michal and Goldberger, Jacob and Greenspan, Hayit},
year = {2018},
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@article{liu_wasserstein_2019,
title = {Wasserstein gan-based small-sample augmentation for new-generation artificial intelligence: a case study of cancer-staging data in biology},
volume = {5},
issn = {2095-8099},
pages = {156--163},
number = {1},
journal = {Engineering},
author = {Liu, Yufei and Zhou, Yuan and Liu, Xin and Dong, Fang and Wang, Chang and Wang, Zihong},
year = {2019},
file = {Liu et al. - 2019 - Wasserstein gan-based small-sample augmentation fo.pdf:/home/clement/Zotero/storage/JWYW25K2/Liu et al. - 2019 - Wasserstein gan-based small-sample augmentation fo.pdf:application/pdf},
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@article{cemgil_autoencoding_nodate,
title = {The Autoencoding Variational Autoencoder},
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% checked
@article{dilokthanakul_deep_2017,
title = {Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders},
journal = {{arXiv}:1611.02648 [cs, stat]},
author = {Dilokthanakul, Nat and Mediano, Pedro A. M. and Garnelo, Marta and Lee, Matthew C. H. and Salimbeni, Hugh and Arulkumaran, Kai and Shanahan, Murray},
year = {2017},
eprinttype = {arxiv},
eprint = {1611.02648},
keywords = {Computer Science - Machine Learning, Statistics - Machine Learning, Computer Science - Neural and Evolutionary Computing},
annotation = {Comment: 12 pages, 6 figures, Under review as a conference paper at {ICLR} 2017},
file = {Dilokthanakul et al. - 2017 - Deep Unsupervised Clustering with Gaussian Mixture.pdf:/home/clement/Zotero/storage/8I7GLJTK/Dilokthanakul et al. - 2017 - Deep Unsupervised Clustering with Gaussian Mixture.pdf:application/pdf},
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@inproceedings{mallasto_wrapped_2018-1,
location = {Salt Lake City, {UT}},
title = {Wrapped Gaussian Process Regression on Riemannian Manifolds},
isbn = {978-1-5386-6420-9},
doi = {10.1109/CVPR.2018.00585},
eventtitle = {2018 {IEEE}/{CVF} Conference on Computer Vision and Pattern Recognition ({CVPR})},
pages = {5580--5588},
booktitle = {2018 {IEEE}/{CVF} Conference on Computer Vision and Pattern Recognition},
publisher = {{IEEE}},
author = {Mallasto, Anton and Feragen, Aasa},
year = {2018},
file = {Mallasto and Feragen - 2018 - Wrapped Gaussian Process Regression on Riemannian .pdf:/home/clement/Zotero/storage/9QH3ESX6/Mallasto and Feragen - 2018 - Wrapped Gaussian Process Regression on Riemannian .pdf:application/pdf},
}

@article{nalisnick_approximate_nodate,
title = {Approximate Inference for Deep Latent Gaussian Mixtures},
pages = {4},
author = {Nalisnick, Eric and Hertel, Lars and Smyth, Padhraic}
}

@inproceedings{zhuang_fmri_2019,
  title={{fMRI data augmentation via synthesis}},
  author={Zhuang, Peiye and Schwing, Alexander G and Koyejo, Oluwasanmi},
  booktitle={2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)},
  pages={1783--1787},
  year={2019},
  organization={IEEE}
}

@inproceedings{hsu_unsupervised_2017-1,
title = {Unsupervised domain adaptation for robust speech recognition via variational autoencoder-based data augmentation},
isbn = {1-5090-4788-3},
eventtitle = {2017 {IEEE} Automatic Speech Recognition and Understanding Workshop ({ASRU})},
pages = {16--23},
publisher = {{IEEE}},
author = {Hsu, Wei-Ning and Zhang, Yu and Glass, James},
year = {2017},
annotation = {Comment: Accepted to {IEEE} {ASRU} 2017},
file = {Hsu et al. - 2017 - Unsupervised Domain Adaptation for Robust Speech R.pdf:/home/clement/Zotero/storage/BAWSMTXS/Hsu et al. - 2017 - Unsupervised Domain Adaptation for Robust Speech R.pdf:application/pdf},
}

@inproceedings{lim_doping_2018-1,
title = {Doping: Generative data augmentation for unsupervised anomaly detection with gan},
isbn = {1-5386-9159-0},
eventtitle = {2018 {IEEE} International Conference on Data Mining ({ICDM})},
pages = {1122--1127},
publisher = {{IEEE}},
author = {Lim, Swee Kiat and Loo, Yi and Tran, Ngoc-Trung and Cheung, Ngai-Man and Roig, Gemma and Elovici, Yuval},
year = {2018},
}

@inproceedings{lim_doping_2018-2,
title = {Doping: Generative data augmentation for unsupervised anomaly detection with gan},
isbn = {1-5386-9159-0},
eventtitle = {2018 {IEEE} International Conference on Data Mining ({ICDM})},
pages = {1122--1127},
publisher = {{IEEE}},
author = {Lim, Swee Kiat and Loo, Yi and Tran, Ngoc-Trung and Cheung, Ngai-Man and Roig, Gemma and Elovici, Yuval},
year = {2018},
annotation = {Comment: Published as a conference paper at {ICDM} 2018 ({IEEE} International Conference on Data Mining)},
file = {Lim et al. - 2018 - DOPING Generative Data Augmentation for Unsupervi.pdf:/home/clement/Zotero/storage/ID4HIR5N/Lim et al. - 2018 - DOPING Generative Data Augmentation for Unsupervi.pdf:application/pdf},
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@inproceedings{mathieu_continuous_2019,
	title = {Continuous hierarchical representations with poincaré variational auto-encoders},
	booktitle = {Advances in neural information processing systems},
	author = {Mathieu, Emile and Le Lan, Charline and Maddison, Chris J and Tomioka, Ryota and Teh, Yee Whye},
	year = {2019},
	pages = {12565--12576},
	file = {Mathieu et al. - Continuous Hierarchical Representations with Poinc.pdf:/home/clement/Zotero/storage/3ACZRAII/Mathieu et al. - Continuous Hierarchical Representations with Poinc.pdf:application/pdf},
}

@inproceedings{nalisnick_approximate_2016,
  title={Approximate inference for deep latent gaussian mixtures},
  author={Nalisnick, Eric and Hertel, Lars and Smyth, Padhraic},
  booktitle={NIPS Workshop on Bayesian Deep Learning},
  volume={2},
  pages={131},
  year={2016}
}


@article{mathieu_riemannian_2020,
  title={Riemannian Continuous Normalizing Flows},
  author={Mathieu, Emile and Nickel, Maximilian},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  year={2020}
}

@article{kobyzev_normalizing_2020,
  title={Normalizing flows: An introduction and review of current methods},
  author={Kobyzev, Ivan and Prince, Simon and Brubaker, Marcus},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2020},
  publisher={IEEE}
}

@article{nielsen_survae_nodate-1,
title = {{SurVAE} Flows: Surjections to Bridge the Gap between {VAEs} and Flows},
pages = {12},
author = {Nielsen, Didrik and Jaini, Priyank and Hoogeboom, Emiel and Winther, Ole and Welling, Max},
file = {Nielsen et al. - SurVAE Flows Surjections to Bridge the Gap betwee.pdf:/home/clement/Zotero/storage/2GEQ3UNI/Nielsen et al. - SurVAE Flows Surjections to Bridge the Gap betwee.pdf:application/pdf},
}

@article{nielsen_survae_2020,
title = {Survae flows: Surjections to bridge the gap between vaes and flows},
volume = {33},
journal = {Advances in Neural Information Processing Systems},
author = {Nielsen, Didrik and Jaini, Priyank and Hoogeboom, Emiel and Winther, Ole and Welling, Max},
year = {2020},
file = {Nielsen et al. - SurVAE Flows Surjections to Bridge the Gap betwee.pdf:/home/clement/Zotero/storage/443SUFU3/Nielsen et al. - SurVAE Flows Surjections to Bridge the Gap betwee.pdf:application/pdf},
}

@article{nielsen_survae_nodate-2,
title = {{SurVAE} Flows: Surjections to Bridge the Gap between {VAEs} and Flows},
pages = {12},
author = {Nielsen, Didrik and Jaini, Priyank and Hoogeboom, Emiel and Winther, Ole and Welling, Max}
}

@inproceedings{ramchandran_longitudinal_2020,
  title={Longitudinal variational autoencoder},
  author={Ramchandran, Siddharth and Tikhonov, Gleb and Kujanp{\"a}{\"a}, Kalle and Koskinen, Miika and L{\"a}hdesm{\"a}ki, Harri},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  pages={3898--3906},
  year={2021},
  organization={PMLR}
}

@article{chen_trajvae_2020,
title = {{TrajVAE}: A Variational {AutoEncoder} model for trajectory generation},
issn = {09252312},
doi = {10.1016/j.neucom.2020.03.120},
pages = {S0925231220312017},
journal = {Neurocomputing},
author = {Chen, Xinyu and Xu, Jiajie and Zhou, Rui and Chen, Wei and Fang, Junhua and Liu, Chengfei},
year = {2020-08},
file = {Chen et al. - 2020 - TrajVAE A Variational AutoEncoder model for traje.pdf:/home/clement/Zotero/storage/JFUVY7DM/Chen et al. - 2020 - TrajVAE A Variational AutoEncoder model for traje.pdf:application/pdf},
}

% checked
@article{qiu_deep_2020,
title = {Deep Latent Variable Model for Learning Longitudinal Multi-view Data},
journal = {{arXiv}:2005.05210 [cs, stat]},
author = {Qiu, Lin and Chinchilli, Vernon M. and Lin, Lin},
year = {2020},
eprinttype = {arxiv},
eprint = {2005.05210},
keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
file = {Qiu et al. - 2020 - Deep Latent Variable Model for Learning Longitudin.pdf:/home/clement/Zotero/storage/I2BNDBYZ/Qiu et al. - 2020 - Deep Latent Variable Model for Learning Longitudin.pdf:application/pdf},
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@inproceedings{lucic_are_2018,
title = {Are {GANs} Created Equal? A Large-Scale Study},
pages = {10},
booktitle = {Advances in Neural Information Processing Systems},
author = {Lucic, Mario and Kurach, Karol and Michalski, Marcin and Gelly, Sylvain and Bousquet, Olivier},
year = {2018},
file = {Lucic et al. - Are GANs Created Equal A Large-Scale Study.pdf:/home/clement/Zotero/storage/55AWRAPF/Lucic et al. - Are GANs Created Equal A Large-Scale Study.pdf:application/pdf;Snapshot:/home/clement/Zotero/storage/NTSUI6IH/fid_score.html:text/html},
}

@online{noauthor_googlecompare_gan_nodate,
title = {google/compare\_gan},
url = {https://github.com/google/compare_gan},
abstract = {Compare {GAN} code. Contribute to google/compare\_gan development by creating an account on {GitHub}.},
titleaddon = {{GitHub}},
urldate = {2021-03-21}
}

@inproceedings{haibo_he_adasyn_2008,
location = {Hong Kong, China},
title = {{ADASYN}: Adaptive synthetic sampling approach for imbalanced learning},
isbn = {978-1-4244-1820-6},
doi = {10.1109/IJCNN.2008.4633969},
shorttitle = {{ADASYN}},
eventtitle = {2008 {IEEE} International Joint Conference on Neural Networks ({IJCNN} 2008 - Hong Kong)},
pages = {1322--1328},
booktitle = {2008 {IEEE} International Joint Conference on Neural Networks ({IEEE} World Congress on Computational Intelligence)},
publisher = {{IEEE}},
author = {{Haibo He} and {Yang Bai} and Garcia, Edwardo A. and {Shutao Li}},
year = {2008},
file = {Haibo He et al. - 2008 - ADASYN Adaptive synthetic sampling approach for i.pdf:/home/clement/Zotero/storage/YEY6NZPU/Haibo He et al. - 2008 - ADASYN Adaptive synthetic sampling approach for i.pdf:application/pdf},
}

@article{chawla_smote_2002,
title = {{SMOTE}: synthetic minority over-sampling technique},
volume = {16},
issn = {1076-9757},
pages = {321--357},
journal = {Journal of artificial intelligence research},
author = {Chawla, Nitesh V and Bowyer, Kevin W and Hall, Lawrence O and Kegelmeyer, W Philip},
year = {2002},
}

@incollection{hutchison_borderline-smote_2005,
location = {Berlin, Heidelberg},
title = {Borderline-{SMOTE}: A New Over-Sampling Method in Imbalanced Data Sets Learning},
volume = {3644},
isbn = {978-3-540-28226-6 978-3-540-31902-3},
pages = {878--887},
booktitle = {Advances in Intelligent Computing},
publisher = {Springer Berlin Heidelberg},
author = {Han, Hui and Wang, Wen-Yuan and Mao, Bing-Huan},
editor = {Huang, De-Shuang and Zhang, Xiao-Ping and Huang, Guang-Bin},
editorb = {Hutchison, David and Kanade, Takeo and Kittler, Josef and Kleinberg, Jon M. and Mattern, Friedemann and Mitchell, John C. and Naor, Moni and Nierstrasz, Oscar and Pandu Rangan, C. and Steffen, Bernhard and Sudan, Madhu and Terzopoulos, Demetri and Tygar, Dough and Vardi, Moshe Y. and Weikum, Gerhard},
editorbtype = {redactor},
year = {2005},
doi = {10.1007/11538059_91},
note = {Series Title: LNCS},
file = {Han et al. - 2005 - Borderline-SMOTE A New Over-Sampling Method in Im.pdf:/home/clement/Zotero/storage/JMJ8V8KM/Han et al. - 2005 - Borderline-SMOTE A New Over-Sampling Method in Im.pdf:application/pdf},
}

@article{barua_mwmote--majority_2012,
title = {{MWMOTE}--majority weighted minority oversampling technique for imbalanced data set learning},
volume = {26},
issn = {1041-4347},
pages = {405--425},
number = {2},
journal = {{IEEE} Transactions on Knowledge and Data Engineering},
author = {Barua, Sukarna and Islam, Md Monirul and Yao, Xin and Murase, Kazuyuki},
year = {2012}
}

@inproceedings{xie_synthetic_2015,
title = {A synthetic minority oversampling method based on local densities in low-dimensional space for imbalanced learning},
eventtitle = {International Conference on Database Systems for Advanced Applications},
pages = {3--18},
publisher = {Springer},
author = {Xie, Zhipeng and Jiang, Liyang and Ye, Tengju and Li, Xiaoli},
year = {2015},
}

@article{douzas_self-organizing_2017,
title = {Self-Organizing Map Oversampling ({SOMO}) for imbalanced data set learning},
volume = {82},
issn = {0957-4174},
pages = {40--52},
journal = {Expert systems with Applications},
author = {Douzas, Georgios and Bacao, Fernando},
year = {2017}
}

@article{bishop_training_1995,
title = {Training with noise is equivalent to Tikhonov regularization},
volume = {7},
issn = {0899-7667},
pages = {108--116},
number = {1},
journal = {Neural Computation},
author = {Bishop, Chris M},
year = {1995},
note = {Publisher: {MIT} Press},
}

@article{fiore_using_2019,
title = {Using generative adversarial networks for improving classification effectiveness in credit card fraud detection},
volume = {479},
issn = {0020-0255},
pages = {448--455},
journal = {Information Sciences},
author = {Fiore, Ugo and De Santis, Alfredo and Perla, Francesca and Zanetti, Paolo and Palmieri, Francesco},
year = {2019}
}

@article{frid-adar_gan-based_2018-1,
title = {{GAN}-based synthetic medical image augmentation for increased {CNN} performance in liver lesion classification},
volume = {321},
issn = {0925-2312},
pages = {321--331},
journal = {Neurocomputing},
author = {Frid-Adar, Maayan and Diamant, Idit and Klang, Eyal and Amitai, Michal and Goldberger, Jacob and Greenspan, Hayit},
year = {2018}
}

@article{frid-adar_gan-based_2018-2,
title = {{GAN}-based synthetic medical image augmentation for increased {CNN} performance in liver lesion classification},
volume = {321},
issn = {0925-2312},
doi = {10.1016/j.neucom.2018.09.013},
pages = {321--331},
journal = {Neurocomputing},
author = {Frid-Adar, Maayan and Diamant, Idit and Klang, Eyal and Amitai, Michal and Goldberger, Jacob and Greenspan, Hayit},
year = {2018},
keywords = {Convolutional neural networks, Data augmentation, Deep learning, Generative adversarial network, Image synthesis, Lesion classification, Liver lesions},
file = {ScienceDirect Snapshot:/home/clement/Zotero/storage/G7KE3PFT/S0925231218310749.html:text/html;Submitted Version:/home/clement/Zotero/storage/TEN7Z7PJ/Frid-Adar et al. - 2018 - GAN-based synthetic medical image augmentation for.pdf:application/pdf;1-s2.0-S0925231218310749-main.pdf:/home/clement/Zotero/storage/6NQDFCZL/1-s2.0-S0925231218310749-main.pdf:application/pdf},
}

@inproceedings{shin_medical_2018,
  title={Medical image synthesis for data augmentation and anonymization using generative adversarial networks},
  author={Shin, Hoo-Chang and Tenenholtz, Neil A and Rogers, Jameson K and Schwarz, Christopher G and Senjem, Matthew L and Gunter, Jeffrey L and Andriole, Katherine P and Michalski, Mark},
  booktitle={{International Workshop on Simulation and Synthesis in Medical Imaging}},
  series={LNCS},
  pages={1--11},
  publisher={Springer},
  year={2018}
}

@article{yi_generative_2019,
title = {Generative adversarial network in medical imaging: A review},
volume = {58},
issn = {1361-8415},
pages = {101552},
journal = {Medical image analysis},
author = {Yi, Xin and Walia, Ekta and Babyn, Paul},
year = {2019}
}

@article{wolterink_generative_2017,
title = {Generative adversarial networks for noise reduction in low-dose {CT}},
volume = {36},
issn = {0278-0062},
pages = {2536--2545},
number = {12},
journal = {{IEEE Transactions on Medical Imaging}},
author = {Wolterink, Jelmer M and Leiner, Tim and Viergever, Max A and Išgum, Ivana},
year = {2017}
}

@incollection{shitrit_accelerated_2017,
title = {Accelerated magnetic resonance imaging by adversarial neural network},
pages = {30--38},
booktitle = {Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support},
publisher = {Springer},
author = {Shitrit, Ohad and Raviv, Tammy Riklin},
year = {2017},
}

@article{wang_3d_2018,
title = {3D conditional generative adversarial networks for high-quality {PET} image estimation at low dose},
volume = {174},
issn = {10538119},
doi = {10.1016/j.neuroimage.2018.03.045},
pages = {550--562},
journal = {{NeuroImage}},
author = {Wang, Yan and Yu, Biting and Wang, Lei and Zu, Chen and Lalush, David S. and Lin, Weili and Wu, Xi and Zhou, Jiliu and Shen, Dinggang and Zhou, Luping},
year = {2018},
file = {Wang et al. - 2018 - 3D conditional generative adversarial networks for.pdf:/home/clement/Zotero/storage/ETIFSPAJ/Wang et al. - 2018 - 3D conditional generative adversarial networks for.pdf:application/pdf},
}

% checked
@article{mahapatra_retinal_2018,
title = {Retinal Vasculature Segmentation Using Local Saliency Maps and Generative Adversarial Networks For Image Super Resolution},
journal = {{arXiv}:1710.04783 [cs]},
author = {Mahapatra, Dwarikanath and Bozorgtabar, Behzad},
year = {2018},
eprinttype = {arxiv},
eprint = {1710.04783},
keywords = {Computer Science - Computer Vision and Pattern Recognition},
annotation = {Comment: Accepted in {MICCAI} 2017 conference},
file = {Mahapatra and Bozorgtabar - 2018 - Retinal Vasculature Segmentation Using Local Salie.pdf:/home/clement/Zotero/storage/ISGJWA82/Mahapatra and Bozorgtabar - 2018 - Retinal Vasculature Segmentation Using Local Salie.pdf:application/pdf},
}

@inproceedings{calimeri_biomedical_2017,
  title={Biomedical data augmentation using generative adversarial neural networks},
  author={Calimeri, Francesco and Marzullo, Aldo and Stamile, Claudio and Terracina, Giorgio},
  booktitle={International conference on artificial neural networks},
  pages={626--634},
  year={2017},
  organization={Springer}
}


@inproceedings{bermudez_learning_2018,
title = {Learning implicit brain {MRI} manifolds with deep learning},
volume = {10574},
eventtitle = {Medical Imaging 2018: Image Processing},
pages = {105741L},
publisher = {International Society for Optics and Photonics},
author = {Bermudez, Camilo and Plassard, Andrew J and Davis, Larry T and Newton, Allen T and Resnick, Susan M and Landman, Bennett A},
year = {2018},
}

% checked
@article{baur_melanogans_2018,
title = {{MelanoGANs}: high resolution skin lesion synthesis with {GANs}},
journal = {{arXiv} preprint {arXiv}:1804.04338},
author = {Baur, Christoph and Albarqouni, Shadi and Navab, Nassir},
year = {2018},
file = {Baur et al. - 2018 - MelanoGANs high resolution skin lesion synthesis .pdf:/home/clement/Zotero/storage/A88F33PT/Baur et al. - 2018 - MelanoGANs high resolution skin lesion synthesis .pdf:application/pdf},
}

@inproceedings{madani_chest_2018,
  title={Chest x-ray generation and data augmentation for cardiovascular abnormality classification},
  author={Madani, Ali and Moradi, Mehdi and Karargyris, Alexandros and Syeda-Mahmood, Tanveer},
  booktitle={Medical Imaging 2018: Image Processing},
  volume={10574},
  pages={105741M},
  year={2018},
  organization={International Society for Optics and Photonics}
}

@article{islam_crash_2021,
title = {Crash data augmentation using variational autoencoder},
volume = {151},
issn = {0001-4575},
pages = {105950},
journal = {Accident Analysis \& Prevention},
author = {Islam, Zubayer and Abdel-Aty, Mohamed and Cai, Qing and Yuan, Jinghui},
year = {2021}
}

@inproceedings{chen_efficient_2018,
title = {Efficient and accurate {MRI} super-resolution using a generative adversarial network and 3D multi-level densely connected network},
eventtitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages = {91--99},
publisher = {Springer},
author = {Chen, Yuhua and Shi, Feng and Christodoulou, Anthony G and Xie, Yibin and Zhou, Zhengwei and Li, Debiao},
year = {2018},
}

@article{kim_improving_2018,
title = {Improving resolution of {MR} images with an adversarial network incorporating images with different contrast},
volume = {45},
issn = {0094-2405},
pages = {3120--3131},
number = {7},
journal = {{Medical Physics}},
author = {Kim, Ki Hwan and Do, Won‐Joon and Park, Sung‐Hong},
year = {2018}
}

% checked
@article{dar_synergistic_2018,
title = {Synergistic reconstruction and synthesis via generative adversarial networks for accelerated multi-contrast {MRI}},
journal = {{arXiv} preprint {arXiv}:1805.10704},
author = {Dar, Salman Ul Hassan and Yurt, Mahmut and Shahdloo, Mohammad and Ildız, Muhammed Emrullah and Çukur, Tolga},
year = {2018},
}

@incollection{shitrit_accelerated_2017-1,
title = {Accelerated magnetic resonance imaging by adversarial neural network},
pages = {30--38},
booktitle = {Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support},
publisher = {Springer},
author = {Shitrit, Ohad and Raviv, Tammy Riklin},
year = {2017},
}

% checked
@article{yi_unsupervised_2018,
title = {Unsupervised and semi-supervised learning with categorical generative adversarial networks assisted by wasserstein distance for dermoscopy image classification},
journal = {{arXiv} preprint {arXiv}:1804.03700},
author = {Yi, Xin and Walia, Ekta and Babyn, Paul},
year = {2018},
}

% checked
@article{korkinof_high-resolution_2018,
title = {High-resolution mammogram synthesis using progressive generative adversarial networks},
journal = {{arXiv} preprint {arXiv}:1807.03401},
author = {Korkinof, Dimitrios and Rijken, Tobias and O'Neill, Michael and Yearsley, Joseph and Harvey, Hugh and Glocker, Ben},
year = {2018},
file = {Korkinof et al. - 2018 - High-resolution mammogram synthesis using progress.pdf:/home/clement/Zotero/storage/L7YMKWLW/Korkinof et al. - 2018 - High-resolution mammogram synthesis using progress.pdf:application/pdf},
}

@inproceedings{salehinejad_generalization_2018,
  title={Generalization of deep neural networks for chest pathology classification in x-rays using generative adversarial networks},
  author={Salehinejad, Hojjat and Valaee, Shahrokh and Dowdell, Tim and Colak, Errol and Barfett, Joseph},
  booktitle={2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={990--994},
  year={2018},
  organization={IEEE}
}

@inproceedings{kwon_generation_2019,
  title={{Generation of 3D brain MRI using auto-encoding generative adversarial networks}},
  author={Kwon, Gihyun and Han, Chihye and Kim, Dae-shik},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={118--126},
  year={2019},
  organization={Springer}
}

@inproceedings{myronenko_3d_2018,
  title={{3D MRI brain tumor segmentation using autoencoder regularization}},
  author={Myronenko, Andriy},
  booktitle={International MICCAI Brainlesion Workshop},
  pages={311--320},
  year={2018},
  organization={Springer}
}

@inproceedings{painchaud_cardiac_2019,
  title={{Cardiac MRI segmentation with strong anatomical guarantees}},
  author={Painchaud, Nathan and Skandarani, Youssef and Judge, Thierry and Bernard, Olivier and Lalande, Alain and Jodoin, Pierre-Marc},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={632--640},
  year={2019},
  organization={Springer}
}

% checked
@article{selvan_lung_2020,
title = {Lung Segmentation from Chest X-rays using Variational Data Imputation},
journal = {{arXiv}:2005.10052 [cs, eess, stat]},
author = {Selvan, Raghavendra and Dam, Erik B. and Detlefsen, Nicki S. and Rischel, Sofus and Sheng, Kaining and Nielsen, Mads and Pai, Akshay},
year = {2020},
eprinttype = {arxiv},
eprint = {2005.10052},
keywords = {Computer Science - Machine Learning, Statistics - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing},
annotation = {Comment: Accepted to be presented at the first Workshop on the Art of Learning with Missing Values (Artemiss) hosted by the 37th International Conference on Machine Learning ({ICML}). Source code, training data and the trained models are available here: https://github.com/raghavian/{lungVAE}/},
file = {Selvan et al. - 2020 - Lung Segmentation from Chest X-rays using Variatio.pdf:/home/clement/Zotero/storage/5BYEUVJ7/Selvan et al. - 2020 - Lung Segmentation from Chest X-rays using Variatio.pdf:application/pdf},
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% checked
@article{arvanitidis_prior-based_2021,
title = {A prior-based approximate latent Riemannian metric},
journal = {{arXiv}:2103.05290 [cs, stat]},
author = {Arvanitidis, Georgios and Georgiev, Bogdan and Schölkopf, Bernhard},
year = {2021},
eprinttype = {arxiv},
eprint = {2103.05290},
keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
file = {Arvanitidis et al. - 2021 - A prior-based approximate latent Riemannian metric.pdf:/home/clement/Zotero/storage/5HZRTW8R/Arvanitidis et al. - 2021 - A prior-based approximate latent Riemannian metric.pdf:application/pdf},
}

@article{arvanitidis_locally_2016,
  title={A locally adaptive normal distribution},
  author={Arvanitidis, Georgios and Hansen, Lars Kai and Hauberg, S{\o}ren},
  journal={Advances in Neural Information Processing Systems},
  pages={4258--4266},
  year={2016},
  publisher={Morgan Kaufmann Publishers, Inc.}
}


@inproceedings{miolane_learning_2020,
  title={Learning Weighted Submanifolds with Variational Autoencoders and Riemannian Variational Autoencoders},
  author={Miolane, Nina and Holmes, Susan},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={14503--14511},
  year={2020}
}

@inproceedings{bauer_resampled_2019-1,
title = {Resampled priors for variational autoencoders},
isbn = {2640-3498},
eventtitle = {The 22nd International Conference on Artificial Intelligence and Statistics},
pages = {66--75},
publisher = {{PMLR}},
author = {Bauer, Matthias and Mnih, Andriy},
year = {2019},
file = {Bauer and Mnih - 2019 - Resampled Priors for Variational Autoencoders.pdf:/home/clement/Zotero/storage/SV65KXZZ/Bauer and Mnih - 2019 - Resampled Priors for Variational Autoencoders.pdf:application/pdf},
}

% checked
@article{chen_variational_2016,
title = {Variational lossy autoencoder},
journal = {{arXiv} preprint {arXiv}:1611.02731},
author = {Chen, Xi and Kingma, Diederik P and Salimans, Tim and Duan, Yan and Dhariwal, Prafulla and Schulman, John and Sutskever, Ilya and Abbeel, Pieter},
year = {2016},
}


@inproceedings{rezende_stochastic_2014-1,
title = {Stochastic backpropagation and approximate inference in deep generative models},
eventtitle = {International conference on machine learning},
pages = {1278--1286},
publisher = {{PMLR}},
author = {Rezende, Danilo Jimenez and Mohamed, Shakir and Wierstra, Daan},
year = {2014},
file = {Rezende et al. - Stochastic Backpropagation and Approximate Inferen.pdf:/home/clement/Zotero/storage/5LK632FU/Rezende et al. - Stochastic Backpropagation and Approximate Inferen.pdf:application/pdf},
}

@article{rezende_stochastic_nodate,
title = {Stochastic Backpropagation and Approximate Inference  in Deep Generative Models},
pages = {9},
author = {Rezende, Danilo Jimenez and Mohamed, Shakir and Wierstra, Daan}
}

@inproceedings{sonderby_ladder_2016,
  title={Ladder Variational Autoencoder},
  author={S{\o}nderby, Casper Kaae and Raiko, Tapani and Maal{\o}e, Lars and S{\o}nderby, S{\o}ren Kaae and Winther, Ole},
  booktitle={29th Annual Conference on Neural Information Processing Systems (NIPS 2016)},
  year={2016}
}

@article{razavi_generating_2019,
	title = {Generating diverse high-fidelity images with vq-vae-2},
	journal = {Advances in Neural Information Processing Systems},
	author = {Razavi, Ali and Oord, Aaron van den and Vinyals, Oriol},
	year = {2020},
	file = {Razavi et al. - 2019 - Generating Diverse High-Fidelity Images with VQ-VA.pdf:/home/clement/Zotero/storage/YXY9K266/Razavi et al. - 2019 - Generating Diverse High-Fidelity Images with VQ-VA.pdf:application/pdf},
}

% checked
@article{aneja_ncp-vae_2020,
title = {{NCP}-{VAE}: Variational Autoencoders with Noise Contrastive Priors},
shorttitle = {{NCP}-{VAE}},
journal = {{arXiv}:2010.02917 [cs, stat]},
author = {Aneja, Jyoti and Schwing, Alexander and Kautz, Jan and Vahdat, Arash},
year = {2020},
eprinttype = {arxiv},
eprint = {2010.02917},
keywords = {Computer Science - Machine Learning, Statistics - Machine Learning, Computer Science - Computer Vision and Pattern Recognition},
annotation = {Comment: 22 pages including appendix},
file = {Aneja et al. - 2020 - NCP-VAE Variational Autoencoders with Noise Contr.pdf:/home/clement/Zotero/storage/AW99LWBH/Aneja et al. - 2020 - NCP-VAE Variational Autoencoders with Noise Contr.pdf:application/pdf},
}

@article{pang_learning_2020,
  title={Learning Latent Space Energy-Based Prior Model},
  author={Pang, Bo and Han, Tian and Nijkamp, Erik and Zhu, Song-Chun and Wu, Ying Nian},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  year={2020}
}

@inproceedings{shmelkov_how_2018,
  title={How good is my GAN?},
  author={Shmelkov, Konstantin and Schmid, Cordelia and Alahari, Karteek},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  pages={213--229},
  year={2018}
}


@incollection{baur_generating_2018,
title = {Generating highly realistic images of skin lesions with {GANs}},
pages = {260--267},
booktitle = {{OR} 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis},
publisher = {Springer},
author = {Baur, Christoph and Albarqouni, Shadi and Navab, Nassir},
year = {2018},
file = {Baur et al. - 2018 - Generating highly realistic images of skin lesions.pdf:/home/clement/Zotero/storage/7KU9DLD9/Baur et al. - 2018 - Generating highly realistic images of skin lesions.pdf:application/pdf},
}

@inproceedings{korkinof_mammogan_2019,
title = {{MammoGAN}: High-resolution synthesis of realistic mammograms},
eventtitle = {International Conference on Medical Imaging with Deep Learning--Extended Abstract Track},
author = {Korkinof, Dimitrios and Heindl, Andreas and Rijken, Tobias and Harvey, Hugh and Glocker, Ben},
year = {2019},
file = {Korkinof et al. - 2019 - MammoGAN High-resolution synthesis of realistic m.pdf:/home/clement/Zotero/storage/BWLDKYHX/Korkinof et al. - 2019 - MammoGAN High-resolution synthesis of realistic m.pdf:application/pdf},
}

@inproceedings{liu_data_2018,
  title={Data augmentation via latent space interpolation for image classification},
  author={Liu, Xiaofeng and Zou, Yang and Kong, Lingsheng and Diao, Zhihui and Yan, Junliang and Wang, Jun and Li, Site and Jia, Ping and You, Jane},
  booktitle={2018 24th International Conference on Pattern Recognition (ICPR)},
  pages={728--733},
  year={2018},
  organization={IEEE}
}

@inproceedings{shin_medical_2018-1,
title = {Medical image synthesis for data augmentation and anonymization using generative adversarial networks},
eventtitle = {International workshop on simulation and synthesis in medical imaging},
pages = {1--11},
publisher = {Springer},
author = {Shin, Hoo-Chang and Tenenholtz, Neil A and Rogers, Jameson K and Schwarz, Christopher G and Senjem, Matthew L and Gunter, Jeffrey L and Andriole, Katherine P and Michalski, Mark},
year = {2018},
file = {Shin et al. - 2018 - Medical image synthesis for data augmentation and .pdf:/home/clement/Zotero/storage/TBHH7T6V/Shin et al. - 2018 - Medical image synthesis for data augmentation and .pdf:application/pdf},
}

@article{waheed_covidgan_2020,
title = {Covidgan: data augmentation using auxiliary classifier gan for improved covid-19 detection},
volume = {8},
issn = {2169-3536},
pages = {91916--91923},
journal = {Ieee Access},
author = {Waheed, Abdul and Goyal, Muskan and Gupta, Deepak and Khanna, Ashish and Al-Turjman, Fadi and Pinheiro, Plácido Rogerio},
year = {2020},
file = {Waheed et al. - 2020 - Covidgan data augmentation using auxiliary classi.pdf:/home/clement/Zotero/storage/LUG9ELVY/Waheed et al. - 2020 - Covidgan data augmentation using auxiliary classi.pdf:application/pdf},
}

@inproceedings{zhu_emotion_2018,
  title={Emotion classification with data augmentation using generative adversarial networks},
  author={Zhu, Xinyue and Liu, Yifan and Li, Jiahong and Wan, Tao and Qin, Zengchang},
  booktitle={Pacific-Asia conference on knowledge discovery and data mining},
  pages={349--360},
  year={2018},
  organization={Springer}
}

@inproceedings{zhu_data_2018,
	title = {Data {Augmentation} using {Conditional} {Generative} {Adversarial} {Networks} for {Leaf} {Counting} in {Arabidopsis} {Plants}.},
	booktitle = {{BMVC}},
	author = {Zhu, Yezi and Aoun, Marc and Krijn, Marcel and Vanschoren, Joaquin and Campus, High Tech},
	year = {2018},
	pages = {324},
	file = {Zhu et al. - 2018 - Data Augmentation using Conditional Generative Adv.pdf:/home/clement/Zotero/storage/ABFYUWXL/Zhu et al. - 2018 - Data Augmentation using Conditional Generative Adv.pdf:application/pdf},
}

@inproceedings{painchaud_cardiac_2019-1,
title = {Cardiac {MRI} segmentation with strong anatomical guarantees},
eventtitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages = {632--640},
publisher = {Springer},
author = {Painchaud, Nathan and Skandarani, Youssef and Judge, Thierry and Bernard, Olivier and Lalande, Alain and Jodoin, Pierre-Marc},
year = {2019},
file = {Painchaud et al. - 2019 - Cardiac MRI Segmentation with Strong Anatomical Gu.pdf:/home/clement/Zotero/storage/UNS9BLPK/Painchaud et al. - 2019 - Cardiac MRI Segmentation with Strong Anatomical Gu.pdf:application/pdf},
}


@inproceedings{salimans_improved_2016,
title = {Improved techniques for training gans},
booktitle = {Advances in Neural Information Processing Systems},
author = {Salimans, Tim and Goodfellow, Ian and Zaremba, Wojciech and Cheung, Vicki and Radford, Alec and Chen, Xi},
year = {2016},
file = {Salimans et al. - 2016 - Improved techniques for training gans.pdf:/home/clement/Zotero/storage/R89UCNQJ/Salimans et al. - 2016 - Improved techniques for training gans.pdf:application/pdf},
}

@inproceedings{heusel_gans_2017,
title = {Gans trained by a two time-scale update rule converge to a local nash equilibrium},
booktitle = {Advances in Neural Information Processing Systems},
author = {Heusel, Martin and Ramsauer, Hubert and Unterthiner, Thomas and Nessler, Bernhard and Hochreiter, Sepp},
year = {2017},
file = {Heusel et al. - 2017 - Gans trained by a two time-scale update rule conve.pdf:/home/clement/Zotero/storage/K6AHGSTL/Heusel et al. - 2017 - Gans trained by a two time-scale update rule conve.pdf:application/pdf},
}

@inproceedings{karras_progressive_2017,
title = {Progressive growing of gans for improved quality, stability, and variation},
booktitle = {International Conference on Learning Representations ({ICLR})},
author = {Karras, Tero and Aila, Timo and Laine, Samuli and Lehtinen, Jaakko},
year = {2017},
file = {Karras et al. - 2017 - Progressive growing of gans for improved quality, .pdf:/home/clement/Zotero/storage/NKRXTNQ7/Karras et al. - 2017 - Progressive growing of gans for improved quality, .pdf:application/pdf},
}

@article{borji_pros_2019,
title = {{Pros and cons of GAN evaluation measures}},
volume = {179},
issn = {1077-3142},
pages = {41--65},
journal = {Computer Vision and Image Understanding},
author = {Borji, Ali},
year = {2019},
file = {Borji - 2019 - Pros and cons of GAN evaluation measures.pdf:/home/clement/Zotero/storage/9FBBLFD6/Borji - 2019 - Pros and cons of GAN evaluation measures.pdf:application/pdf},
}

@article{borji_pros_2019-1,
title = {Pros and cons of {GAN} evaluation measures},
volume = {179},
issn = {10773142},
doi = {10.1016/j.cviu.2018.10.009},
pages = {41--65},
journal = {Computer Vision and Image Understanding},
author = {Borji, Ali},
year = {2019}
}

@article{fernandez_smote_2018,
	title = {{SMOTE} for learning from imbalanced data: progress and challenges, marking the 15-year anniversary},
	volume = {61},
	issn = {1076-9757},
	pages = {863--905},
	journal = {Journal of artificial intelligence research},
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	year = {2018},
	file = {Fernández et al. - 2018 - SMOTE for learning from imbalanced data progress .pdf:/home/clement/Zotero/storage/37LKAKUS/Fernández et al. - 2018 - SMOTE for learning from imbalanced data progress .pdf:application/pdf},
}

@article{blagus_smote_2013,
	title = {{SMOTE} for high-dimensional class-imbalanced data},
	volume = {14},
	issn = {1471-2105},
	doi = {10.1186/1471-2105-14-106},
	pages = {106},
	number = {1},
	journal = {{BMC} Bioinformatics},
	author = {Blagus, Rok and Lusa, Lara},
	year = {2013},
	file = {Blagus and Lusa - 2013 - SMOTE for high-dimensional class-imbalanced data.pdf:/home/clement/Zotero/storage/BKZ4XUS6/Blagus and Lusa - 2013 - SMOTE for high-dimensional class-imbalanced data.pdf:application/pdf},
}

@article{nguyen_borderline_2011,
	title = {Borderline over-sampling for imbalanced data classification},
	volume = {3},
	issn = {1755-3210},
	pages = {4--21},
	number = {1},
	journal = {International Journal of Knowledge Engineering and Soft Data Paradigms},
	author = {Nguyen, Hien M and Cooper, Eric W and Kamei, Katsuari},
	year = {2011},
	file = {Nguyen et al. - 2011 - Borderline over-sampling for imbalanced data class.pdf:/home/clement/Zotero/storage/U6GWZKCP/Nguyen et al. - 2011 - Borderline over-sampling for imbalanced data class.pdf:application/pdf},
}




% Elina

@article{ellis_australian_2009,
	title = {The {Australian} {Imaging}, {Biomarkers} and {Lifestyle} ({AIBL}) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of {Alzheimer}'s disease},
	volume = {21},
	issn = {1041-6102},
	shorttitle = {The {Australian} {Imaging}, {Biomarkers} and {Lifestyle} ({AIBL}) study of aging},
	doi = {10.1017/S1041610209009405},
	number = {4},
	journal = {International Psychogeriatrics},
	author = {Ellis, Kathryn A. and Bush, Ashley I. and Darby, David and De Fazio, Daniela and Foster, Jonathan and Hudson, Peter and Lautenschlager, Nicola T. and Lenzo, Nat and Martins, Ralph N. and Maruff, Paul and Masters, Colin and Milner, Andrew and Pike, Kerryn and Rowe, Christopher and Savage, Greg and Szoeke, Cassandra and Taddei, Kevin and Villemagne, Victor and Woodward, Michael and Ames, David and {AIBL Research Group}},
	year = {2009},
	pmid = {19470201},
	keywords = {Aged, Aged, 80 and over, Alzheimer Disease, Apolipoprotein E4, Australia, Biomarkers, Brain, Cognition Disorders, Cohort Studies, Female, Humans, Life Style, Longitudinal Studies, Magnetic Resonance Imaging, Male, Mass Screening, Mental Status Schedule, Middle Aged, Neuropsychological Tests, Patient Selection, Positron-Emission Tomography, Psychometrics, Reference Values, Risk Factors},
	pages = {672--687},
	file = {Ellis et al. - 2009 - The Australian Imaging, Biomarkers and Lifestyle (.pdf:/Users/elina.thibeausutre/Zotero/storage/YBNRBUML/Ellis et al. - 2009 - The Australian Imaging, Biomarkers and Lifestyle (.pdf:application/pdf}
}

@article{tustison_n4itk_2010,
	title = {{N4ITK}: {Improved} {N3} {Bias} {Correction}},
	volume = {29},
	issn = {0278-0062, 1558-254X},
	shorttitle = {{N4ITK}},
	doi = {10.1109/TMI.2010.2046908},
	number = {6},
	journal = {IEEE Transactions on Medical Imaging},
	author = {Tustison, Nicholas J and Avants, Brian B and Cook, Philip A and {Yuanjie Zheng} and Egan, Alexander and Yushkevich, Paul A and Gee, James C},
	year = {2010},
	pages = {1310--1320},
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}

@article{bergstra_random_2012,
	title = {Random {Search} for {Hyper}-{Parameter} {Optimization}},
	volume = {13},
	issn = {ISSN 1533-7928},
	number = {Feb},
	journal = {Journal of Machine Learning Research},
	author = {Bergstra, James and Bengio, Yoshua},
	year = {2012},
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}

@inproceedings{he_delving_2015,
	address = {Santiago, Chile},
	title = {Delving {Deep} into {Rectifiers}: {Surpassing} {Human}-{Level} {Performance} on {ImageNet} {Classification}},
	isbn = {978-1-4673-8391-2},
	shorttitle = {Delving {Deep} into {Rectifiers}},
	doi = {10.1109/ICCV.2015.123},
	booktitle = {2015 {IEEE} {International} {Conference} on {Computer} {Vision} ({ICCV})},
	publisher = {IEEE},
	author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
	year = {2015},
	pages = {1026--1034},
	file = {He et al. - 2015 - Delving Deep into Rectifiers Surpassing Human-Lev.pdf:/Users/elina.thibeausutre/Zotero/storage/TRGJCEMZ/He et al. - 2015 - Delving Deep into Rectifiers Surpassing Human-Lev.pdf:application/pdf}
}

@article{wen_convolutional_2020,
	title = {Convolutional neural networks for classification of {Alzheimer}'s disease: {Overview} and reproducible evaluation},
	volume = {63},
	issn = {1361-8415},
	shorttitle = {Convolutional neural networks for classification of {Alzheimer}'s disease},
	doi = {10.1016/j.media.2020.101694},
	journal = {Medical Image Analysis},
	author = {Wen, Junhao and Thibeau-Sutre, Elina and Diaz-Melo, Mauricio and Samper-González, Jorge and Routier, Alexandre and Bottani, Simona and Dormont, Didier and Durrleman, Stanley and Burgos, Ninon and Colliot, Olivier},
	year = {2020},
	keywords = {Reproducibility, Statistics - Machine Learning, Alzheimer's disease classification, Magnetic resonance imaging, Computer Science - Machine Learning, Convolutional neural network, Electrical Engineering and Systems Science - Image and Video Processing, Alzheimer's disease classification Magnetic resonance imaging, Convolutional neural network Reproducibility},
	pages = {101694},
	file = {arXiv.org Snapshot:/Users/elina.thibeausutre/Zotero/storage/N9T5UZ5U/1904.html:text/html;ScienceDirect Full Text PDF:/Users/elina.thibeausutre/Zotero/storage/QP9ADF8C/Wen et al. - 2020 - Convolutional neural networks for classification o.pdf:application/pdf;ScienceDirect Snapshot:/Users/elina.thibeausutre/Zotero/storage/PP2FXGT8/S1361841520300591.html:text/html;ScienceDirect Snapshot:/Users/elina.thibeausutre/Zotero/storage/2J492744/S1361841520300591.html:text/html;Wen et al. - 2019 - Convolutional Neural Networks for Classification o.pdf:/Users/elina.thibeausutre/Zotero/storage/R7ATB29Y/Wen et al. - 2019 - Convolutional Neural Networks for Classification o.pdf:application/pdf}
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@article{avants_insight_2014,
	title = {The {Insight} {ToolKit} image registration framework},
	volume = {8},
	issn = {1662-5196},
	doi = {10.3389/fninf.2014.00044},
	journal = {Frontiers in Neuroinformatics},
	author = {Avants, Brian B. and Tustison, Nicholas J. and Stauffer, Michael and Song, Gang and Wu, Baohua and Gee, James C.},
	year = {2014},
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}

@article{fonov_deep_2018,
	title = {Deep learning of quality control for stereotaxic registration of human brain {MRI}},
	copyright = {© 2018, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/},
	doi = {10.1101/303487},
	journal = {bioRxiv},
	author = {Fonov, Vladimir S. and Dadar, Mahsa and Group, The PREVENT-AD Research and Collins, D. Louis},
	year = {2018},
	pages = {303487},
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}

@article{fonov_unbiased_2011,
	title = {Unbiased average age-appropriate atlases for pediatric studies},
	volume = {54},
	issn = {1053-8119},
	doi = {10.1016/j.neuroimage.2010.07.033},
	number = {1},
	journal = {NeuroImage},
	author = {Fonov, Vladimir and Evans, Alan C. and Botteron, Kelly and Almli, C. Robert and McKinstry, Robert C. and Collins, D. Louis},
	year = {2011},
	keywords = {Registration, Atlas template, Pediatric image analysis},
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@article{fonov_unbiased_2009,
	series = {Organization for {Human} {Brain} {Mapping} 2009 {Annual} {Meeting}},
	title = {Unbiased nonlinear average age-appropriate brain templates from birth to adulthood},
	volume = {47},
	issn = {1053-8119},
	doi = {10.1016/S1053-8119(09)70884-5},
	journal = {NeuroImage},
	author = {Fonov, VS and Evans, AC and McKinstry, RC and Almli, CR and Collins, DL},
	year = {2009},
	pages = {S102},
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}

@article{gorgolewski_brain_2016,
	title = {The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments},
	volume = {3},
	copyright = {2016 The Author(s)},
	issn = {2052-4463},
	doi = {10.1038/sdata.2016.44},
	number = {1},
	journal = {Scientific Data},
	author = {Gorgolewski, Krzysztof J. and Auer, Tibor and Calhoun, Vince D. and Craddock, R. Cameron and Das, Samir and Duff, Eugene P. and Flandin, Guillaume and Ghosh, Satrajit S. and Glatard, Tristan and Halchenko, Yaroslav O. and Handwerker, Daniel A. and Hanke, Michael and Keator, David and Li, Xiangrui and Michael, Zachary and Maumet, Camille and Nichols, B. Nolan and Nichols, Thomas E. and Pellman, John and Poline, Jean-Baptiste and Rokem, Ariel and Schaefer, Gunnar and Sochat, Vanessa and Triplett, William and Turner, Jessica A. and Varoquaux, Gaël and Poldrack, Russell A.},
	year = {2016},
	pages = {160044},
	file = {Full Text PDF:/Users/elina.thibeausutre/Zotero/storage/4WMJDLPH/Gorgolewski et al. - 2016 - The brain imaging data structure, a format for org.pdf:application/pdf;Snapshot:/Users/elina.thibeausutre/Zotero/storage/ZDXKC2LT/sdata201644.html:text/html}
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@article{shorten_survey_2019,
	title = {A survey on {Image} {Data} {Augmentation} for {Deep} {Learning}},
	volume = {6},
	issn = {2196-1115},
	doi = {10.1186/s40537-019-0197-0},
	number = {1},
	journal = {Journal of Big Data},
	author = {Shorten, Connor and Khoshgoftaar, Taghi M.},
	year = {2019},
	pages = {60},
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@inproceedings{aderghal_classification_2018,
	title = {Classification of {Alzheimer} {Disease} on {Imaging} {Modalities} with {Deep} {CNNs} {Using} {Cross}-{Modal} {Transfer} {Learning}},
	doi = {10.1109/CBMS.2018.00067},
	booktitle = {2018 {IEEE} 31st {International} {Symposium} on {Computer}-{Based} {Medical} {Systems} ({CBMS})},
	author = {Aderghal, K. and Khvostikov, A. and Krylov, A. and Benois-Pineau, J. and Afdel, K. and Catheline, G.},
	year = {2018},
	note = {ISSN: 2372-9198},
	keywords = {Alzheimer's disease, Alzheimer’s Disease, Biomedical imaging, Convolutional Neural Networks, Deep Learning, Diffusion tensor imaging, Hippocampus, Medical Imaging, Mild Cognitive Impairment, Multi-Modal, Training, Transfer Learning},
	pages = {345--350},
	file = {IEEE Xplore Abstract Record:/Users/elina.thibeausutre/Zotero/storage/FBMJBV2G/8417262.html:text/html}
}




% Ninon
@inproceedings{bi_synthesis_2017,
  title = {Synthesis of {{Positron Emission Tomography}} ({{PET}}) {{Images}} via {{Multi}}-Channel {{Generative Adversarial Networks}} ({{GANs}})},
  booktitle = {Molecular {{Imaging}}, {{Reconstruction}} and {{Analysis}} of {{Moving Body Organs}}, and {{Stroke Imaging}} and {{Treatment}}},
  author = {Bi, Lei and Kim, Jinman and Kumar, Ashnil and Feng, Dagan and Fulham, Michael},
  year = {2017},
  pages = {43--51},
  publisher = {{Springer}},
  doi = {10.1007/978-3-319-67564-0_5},
  series = {{LNCS}}
}

% checked
@article{mariani_bagan_2018,
  title = {{{BAGAN}}: {{Data Augmentation}} with {{Balancing GAN}}},
  author = {Mariani, Giovanni and Scheidegger, Florian and Istrate, Roxana and Bekas, Costas and Malossi, Cristiano},
  year = {2018},
  journal = {arXiv:1803.09655}
}

@article{xue_selective_2021,
  title = {Selective {{Synthetic Augmentation}} with {{HistoGAN}} for {{Improved Histopathology Image Classification}}},
  author = {Xue, Yuan and Ye, Jiarong and Zhou, Qianying and Long, L. Rodney and Antani, Sameer and Xue, Zhiyun and Cornwell, Carl and Zaino, Richard and Cheng, Keith C. and Huang, Xiaolei},
  year = {2021},
  volume = {67},
  pages = {101816},
  doi = {10.1016/j.media.2020.101816},
  journal = {Medical Image Analysis}
}

@article{pesteie_adaptive_2019,
  title = {Adaptive {{Augmentation}} of {{Medical Data Using Independently Conditional Variational Auto}}-{{Encoders}}},
  author = {Pesteie, Mehran and Abolmaesumi, Purang and Rohling, Robert N.},
  year = {2019},
  volume = {38},
  pages = {2807--2820},
  doi = {10.1109/TMI.2019.2914656},
  journal = {IEEE Transactions on Medical Imaging},
  number = {12}
}


@article{nalepa_data_2019,
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	issn = {1662-5188},
	shorttitle = {Data {Augmentation} for {Brain}-{Tumor} {Segmentation}},
	doi = {10.3389/fncom.2019.00083},
	journal = {Frontiers in Computational Neuroscience},
	author = {Nalepa, Jakub and Marcinkiewicz, Michal and Kawulok, Michal},
	year = {2019},
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@article{oh_classification_2019,
	title = {Classification and {Visualization} of {Alzheimer}'s {Disease} using {Volumetric} {Convolutional} {Neural} {Network} and {Transfer} {Learning}},
	volume = {9},
	issn = {2045-2322},
	doi = {10.1038/s41598-019-54548-6},
	number = {1},
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	doi = {10.1186/s40708-020-00104-2},
	number = {1},
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	author = {Islam, J. and Zhang, Y.},
	year = {2020},
	keywords = {Alzheimer’s disease, Brain imaging, Generative adversarial networks, Positron emission tomography (PET), Synthetic medical image generation},
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@article{liu_weakly_2020,
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	journal = {IEEE Transactions on Cybernetics},
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	year = {2020},
	keywords = {Alzheimer's disease (AD), clinical score, disease prognosis, neural network, weakly supervised learning},
	pages = {3381--3392},
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	title = {Deep {Residual} {Nets} for {Improved} {Alzheimer}'s {Diagnosis}},
	isbn = {978-1-4503-4722-8},
	doi = {10.1145/3107411.3108224},
	booktitle = {8th {ACM} {International} {Conference} on {Bioinformatics}, {Computational} {Biology},and {Health} {Informatics}  - {ACM}-{BCB} '17},
	publisher = {ACM Press},
	author = {Valliani, Aly and Soni, Ameet},
	year = {2017},
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	pages = {615--615}
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@inproceedings{backstrom_efficient_2018,
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	volume = {2018-April},
	doi = {10.1109/ISBI.2018.8363543},
	booktitle = {2018 {IEEE} 15th {International} {Symposium} on {Biomedical} {Imaging} ({ISBI} 2018)},
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	keywords = {3D deep convolutional networks, Already integrated, Alzheimer's disease detection, Automatic feature learning, Computer-aided diagnosis, Deep learning, MR imaging},
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	booktitle = {2017 10th {International} {Congress} on {Image} and {Signal} {Processing}, {BioMedical} {Engineering} and {Informatics} ({CISP}-{BMEI})},
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}
